Formulation of HPV Dry Powder Wafers for Sublingual Vaccination

Vaccination has been used to prevent diseases since the early 1800s.Throughout the history of vaccination, attention has only been paid to the type of virus that can be administered instead of the method of vaccination. Traditional needle-dependent vaccination requires sanitation and refrigeration which is not available in developing countries such as India, where viruses such as the human papillomavirus (HPV) are very prevalent. The goal of this study was to develop a sublingual wafer formulation that is robust and heat-stable. The sublingual wafer delivery method is expected to lower the cost of vaccination because of the ease of transportation and storage. Dry powder vaccine formulations were prepared using the Sievers et al patented Carbon Dioxide Assisted Nebulization with a Bubble Dryer (CAN- BD) drying process. Excipients commonly used in the pharmaceutical industry for oral tablet formation were tested for wafer formation with the CAN-BD processed protein powder. Desired wafer properties were tested, i.e. binding capabilities, target dissociation time

Concept learning of text documents

Concept learning of text documents can be viewed as the problem of acquiring the definition of a general category of documents. To definite the category of a text document, the Conjunctive of keywords is usually be used. These keywords should be fewer and comprehensible. A na&iuml;ve method is enumerating all combinations of keywords to extract suitable ones. However, because of the enormous number of keyword combinations, it is impossible to extract the most relevant keywords to describe the categories of documents by enumerating all possible combinations of keywords. Many heuristic methods are proposed, such as GA-base, immune based algorithm. In this work, we introduce pruning power technique and propose a robust enumeration-based concept learning algorithm. Experimental results show that the rules produce by our approach has more comprehensible and simplicity than by other methods. <br /

Finding short patterns to classify text documents

Many classification methods have been proposed to find patterns in text documents. However, according to Occam\u27s razor principle, &quot;the explanation of any phenomenon should make as few assumptions as possible&quot;, short patterns usually have more explainable and meaningful for classifying text documents. In this paper, we propose a depth-first pattern generation algorithm, which can find out short patterns from text document more effectively, comparing with breadth-first algorithm <br /

Finding coverage using incremental attribute combinations

Coverage is the range that covers only positive samples in attribute (or feature) space. Finding coverage is the kernel problem in induction algorithms because of the fact that coverage can be used as rules to describe positive samples. To reflect the characteristic of training samples, it is desirable that the large coverage that cover more positive samples. However, it is difficult to find large coverage, because the attribute space is usually very high dimensionality. Many heuristic methods such as ID3, AQ and CN2 have been proposed to find large coverage. A robust algorithm also has been proposed to find the largest coverage, but the complexities of time and space are costly when the dimensionality becomes high. To overcome this drawback, this paper proposes an algorithm that adopts incremental feature combinations to effectively find the largest coverage. In this algorithm, the irrelevant coverage can be pruned away at early stages because potentially large coverage can be found earlier. Experiments show that the space and time needed to find the largest coverage has been significantly reduced.<br /

Finding rule groups to classify high dimensional gene expression datasets

Microarray data provides quantitative information about the transcription profile of cells. To analyze microarray datasets, methodology of machine learning has increasingly attracted bioinformatics researchers. Some approaches of machine learning are widely used to classify and mine biological datasets. However, many gene expression datasets are extremely high dimensionality, traditional machine learning methods can not be applied effectively and efficiently. This paper proposes a robust algorithm to find out rule groups to classify gene expression datasets. Unlike the most classification algorithms, which select dimensions (genes) heuristically to form rules groups to identify classes such as cancerous and normal tissues, our algorithm guarantees finding out best-k dimensions (genes), which are most discriminative to classify samples in different classes, to form rule groups for the classification of expression datasets. Our experiments show that the rule groups obtained by our algorithm have higher accuracy than that of other classification approaches <br /

Keyword extraction for text categorization

Text categorization (TC) is one of the main applications of machine learning. Many methods have been proposed, such as Rocchio method, Naive bayes based method, and SVM based text classification method. These methods learn labeled text documents and then construct a classifier. A new coming text document\u27s category can be predicted. However, these methods do not give the description of each category. In the machine learning field, there are many concept learning algorithms, such as, ID3 and CN2. This paper proposes a more robust algorithm to induce concepts from training examples, which is based on enumeration of all possible keywords combinations. Experimental results show that the rules produced by our approach have more precision and simplicity than that of other methods.<br /

Exploring Asymmetric Tunable Blind-Spots for Self-supervised Denoising in Real-World Scenarios

Self-supervised denoising has attracted widespread attention due to its ability to train without clean images. However, noise in real-world scenarios is often spatially correlated, which causes many self-supervised algorithms based on the pixel-wise independent noise assumption to perform poorly on real-world images. Recently, asymmetric pixel-shuffle downsampling (AP) has been proposed to disrupt the spatial correlation of noise. However, downsampling introduces aliasing effects, and the post-processing to eliminate these effects can destroy the spatial structure and high-frequency details of the image, in addition to being time-consuming. In this paper, we systematically analyze downsampling-based methods and propose an Asymmetric Tunable Blind-Spot Network (AT-BSN) to address these issues. We design a blind-spot network with a freely tunable blind-spot size, using a large blind-spot during training to suppress local spatially correlated noise while minimizing damage to the global structure, and a small blind-spot during inference to minimize information loss. Moreover, we propose blind-spot self-ensemble and distillation of non-blind-spot network to further improve performance and reduce computational complexity. Experimental results demonstrate that our method achieves state-of-the-art results while comprehensively outperforming other self-supervised methods in terms of image texture maintaining, parameter count, computation cost, and inference time

Causal-Based Supervision of Attention in Graph Neural Network: A Better and Simpler Choice towards Powerful Attention

Recent years have witnessed the great potential of attention mechanism in graph representation learning. However, while variants of attention-based GNNs are setting new benchmarks for numerous real-world datasets, recent works have pointed out that their induced attentions are less robust and generalizable against noisy graphs due to lack of direct supervision. In this paper, we present a new framework which utilizes the tool of causality to provide a powerful supervision signal for the learning process of attention functions. Specifically, we estimate the direct causal effect of attention to the final prediction, and then maximize such effect to guide attention attending to more meaningful neighbors. Our method can serve as a plug-and-play module for any canonical attention-based GNNs in an end-to-end fashion. Extensive experiments on a wide range of benchmark datasets illustrated that, by directly supervising attention functions, the model is able to converge faster with a clearer decision boundary, and thus yields better performances

Concept Learning of Text Documents

Abstrac